System and Method for Wafer Inspection with a Noise Boundary Threshold

ABSTRACT

A method includes receiving one or more images of three or more die of a wafer, determining a median intensity value of a set of pixel intensity values acquired from a same location on each of the three or more die, determining a difference intensity value for the set of pixel intensity values by comparing the median intensity value of the set of pixel intensity values to each pixel intensity value, grouping the pixel intensity values into an intensity bin based on the median intensity value of the set of pixel intensity values, generating an initial noise boundary based on a selected difference intensity value in the intensity bin, generating a final noise boundary by adjusting the initial noise boundary, generating a detection boundary by applying a threshold to the final noise boundary, and classifying one or more pixel intensity values outside the detection boundary as a defect.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority under 35 U.S.C. §119(e) to U.S.Provisional Patent Application Ser. No. 62/317,927, filed Apr. 4, 2016,titled NOISE BOUNDARY THRESHOLDING (NBT) ALGORITHM FOR LS PLATFORMDEFECT DETECTION, naming Xuguang Jiang and Yong Zhang as inventors,which is incorporated herein by reference in the entirety.

TECHNICAL FIELD

The present invention generally relates to wafer inspection and review,and more particularly, to the generation and implementation of a noiseboundary threshold (NBT) procedure during wafer inspection and review.

BACKGROUND

Fabricating semiconductor devices such as logic and memory devicestypically includes processing a substrate such as a semiconductor waferusing a large number of semiconductor fabrication procedures to formvarious features and multiple levels of the semiconductor devices.Multiple semiconductor devices may be fabricated in an arrangement on asingle semiconductor wafer and then separated into individualsemiconductor devices.

Semiconductor devices may develop defects during the fabricationprocedures. Inspection procedures are performed at various steps duringa semiconductor manufacturing process to detect defects on a specimen.Inspection procedures are an important part of fabricating semiconductordevices such as integrated circuits, becoming even more important tosuccessfully manufacture acceptable semiconductor devices as thedimensions of semiconductor devices decrease. For instance, detection ofdefects has become highly desirable as the dimensions of semiconductordevices decrease, as even relatively small defects may cause unwantedaberrations in the semiconductor devices.

One method of defect detection includes checking one or more patterns ofreview (POR) with one or more procedures, including the Hierarchical andLocal Automatic Thresholding (HLAT) and Fast Adaptive SegmentedThresholding (FAST) procedures. The HLAT procedure is highly sensitiveand includes multiple non-default (i.e., tunable) operation parameters(e.g., system noise, wafer noise, pattern noise, and the like). As aresult, the HLAT procedure is difficult to tune, and is user-dependent.

In contrast, the FAST procedure includes only a single non-default(i.e., tunable) operation parameter and so is quickly tuned, but is oflower sensitivity than the HLAT procedure. Additionally, the FASTprocedure may include a sensitivity gap as opposed to the HLAT procedureor a broadband plasma (BBP) inspection system.

Both the HLAT and FAST procedures require a number of ideal-scenarioassumptions to be made about the one or more wafers being inspected. Forexample, both the HLAT and FAST procedures assume that the noisedistribution in one or more dies of the one or more wafers is the same.By way of another example, both the HLAT and FAST procedures use acommon noise threshold parameter for the one or more dies. By way ofanother example, both the HLAT and FAST procedure use a common noisethreshold parameter for both bright noise and dark noise defects. It isnoted herein that both the HLAT and FAST include a dark defect factorfor both algorithms to detune dark defects; however, the dark defectfactor cannot detect dark defects if the factor is below a bright noisethreshold. Real-world scenarios, however, may include uneven noisedistributions, uneven bright noise distributions, and/or uneven darknoise distributions. For example, dies adjacent to a particular die on awafer may include color variation. By way of another example, the one ormore dies may have one or more weak signal defects buried underneath oneor stronger signal defects. By way of another example, the one or moredies may be dominated by bright noise and/or dark noise.

The FAST procedure requires an additional ideal-scenario assumption thatbright noise and dark noise follow a Gaussian distribution (e.g.,Gaussian mean and standard deviation distribution levels) scheme in allthree die. Real-world scenarios, however, may include color variation inthe adjacent dies to a particular die that prevents bright noise anddark noise from following Gaussian distribution curves.

As such, applying ideal-scenario assumptions to real-world scenariosoften result in missing defects of interest (DOI) and a high nuisancerate during wafer inspection and review. Therefore, it would bedesirable to provide a system and method that cures the shortcomings ofthe previous approaches as identified above.

SUMMARY

A wafer inspection system is disclosed, in accordance with one or moreembodiments of the present disclosure. In one illustrative embodiment,the system includes an inspection sub-system. In another illustrativeembodiment, the system includes a stage configured to secure a wafer. Inanother illustrative embodiment, the system includes a controllercommunicatively coupled to the inspection sub-system. In anotherillustrative embodiment, the controller includes one or more processorsconfigured to execute a set of program instructions stored in memory. Inanother illustrative embodiment, the program instructions are configuredto cause the one or more processors to receive one or more images ofthree or more die of the wafer from the inspection sub-system. Inanother illustrative embodiment, the program instructions are configuredto cause the one or more processors to determine a median intensityvalue of a set of pixel intensity values acquired from a same locationon each of the three or more die. In another illustrative embodiment,the program instructions are configured to cause the one or moreprocessors to determine a difference intensity value for the set ofpixel intensity values by comparing the median intensity value of theset of pixel intensity values to each pixel intensity value. In anotherillustrative embodiment, the program instructions are configured tocause the one or more processors to group the pixel intensity valuesinto an intensity bin based on the median intensity value of the set ofpixel intensity values. In another illustrative embodiment, the programinstructions are configured to cause the one or more processors togenerate an initial noise boundary based on a selected differenceintensity value in the intensity bin. In another illustrativeembodiment, the program instructions are configured to cause the one ormore processors to generate a final noise boundary by adjusting theinitial noise boundary. In another illustrative embodiment, the programinstructions are configured to cause the one or more processors togenerate a detection boundary by applying a threshold to the final noiseboundary. In another illustrative embodiment, the program instructionsare configured to cause the one or more processors to classify one ormore pixel intensity values outside the detection boundary as a defect.

A method is disclosed, in accordance with one or more embodiments of thepresent disclosure. In one illustrative embodiment, the method includesreceiving one or more images of three or more die of the wafer from aninspection sub-system. In another illustrative embodiment, the methodincludes determining a median intensity value of a set of pixelintensity values acquired from a same location on each of the three ormore die. In another illustrative embodiment, the method includesdetermining a difference intensity value for the set of pixel intensityvalues by comparing the median intensity value of the set of pixelintensity values to each pixel intensity value. In another illustrativeembodiment, the method includes grouping the pixel intensity values intoan intensity bin based on the median intensity value of the set of pixelintensity values. In another illustrative embodiment, the methodincludes generating an initial noise boundary based on a selecteddifference intensity value in the intensity bin. In another illustrativeembodiment, the method includes generating a final noise boundary byadjusting the initial noise boundary. In another illustrativeembodiment, the method includes generating a detection boundary byapplying a threshold to the final noise boundary. In anotherillustrative embodiment, the method includes classifying one or morepixel intensity values outside the detection boundary as a defect.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not necessarily restrictive of the present disclosure. Theaccompanying drawings, which are incorporated in and constitute a partof the characteristic, illustrate subject matter of the disclosure.Together, the descriptions and the drawings serve to explain theprinciples of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The numerous advantages of the disclosure may be better understood bythose skilled in the art by reference to the accompanying figures inwhich:

FIG. 1 illustrates a block diagram view of a system for imaging asample, in accordance with one or more embodiments of the presentdisclosure.

FIG. 2 illustrates a flow diagram depicting a method of locating one ormore defects with a boundary thresholding procedure, in accordance withone or more embodiments of the present disclosure.

FIG. 3A illustrates graphical data including data for one or more pixelsamples, in accordance with one or more embodiments of the presentdisclosure.

FIG. 3B illustrates graphical data including one or more initial noiseboundaries, in accordance with one or more embodiments of the presentdisclosure.

FIG. 3C illustrates graphical data including one or more noiseboundaries, in accordance with one or more embodiments of the presentdisclosure.

FIG. 3D illustrates graphical data including one or more noiseboundaries, in accordance with one or more embodiments of the presentdisclosure.

FIG. 4A illustrates graphical data including one or more noiseboundaries, in accordance with one or more embodiments of the presentdisclosure.

FIG. 4B illustrates graphical data including one or more noiseboundaries, in accordance with one or more embodiments of the presentdisclosure.

FIG. 5A illustrates graphical data including one or more noiseboundaries, in accordance with one or more embodiments of the presentdisclosure.

FIG. 5B illustrates graphical data including one or more noiseboundaries, in accordance with one or more embodiments of the presentdisclosure.

FIG. 5C illustrates graphical data including one or more noiseboundaries, in accordance with one or more embodiments of the presentdisclosure.

DETAILED DESCRIPTION OF THE INVENTION

Reference will now be made in detail to the subject matter disclosed,which is illustrated in the accompanying drawings.

Referring to FIGS. 1 through 5C, systems and methods for generating andimplementing a noise boundary threshold procedure during waferinspection and review are disclosed, in accordance with one or moreembodiments of the present disclosure.

Embodiments of the present disclosure are directed to providing a waferinspection procedure with improved defect detection capabilities.Embodiments of the present disclosure are also directed to making one ormore operational assumptions that more closely resemble real-worldscenarios that may occur during wafer inspection and review. Embodimentsof the present disclosure are also directed to detecting one or moretypes of defects on wafers that may be missed by known laser-scanningdefect detection procedures, including the HLAT and FAST procedures.

Embodiments of the present disclosure are also directed to generatingone or more noise boundaries for the wafer inspection data. Embodimentsof the present disclosure are also directed to adjusting the one or morenoise boundaries to be adaptive to noise. Embodiments of the presentdisclosure are also directed to applying a threshold to the adjusted oneor more noise boundaries to generate a threshold boundary to separateinspection system noise from one or more true defects.

Advantages of one or more embodiments of the present disclosure includeimproved defect detection sensitivity. Advantages of one or moreembodiments of the present disclosure also include an increasedadaptability to various types of wafer inspection system noise. Forexample, advantages of one or more embodiments of the present disclosurealso include detecting one or more weak signal defects. By way ofanother example, advantages of one or more embodiments of the presentdisclosure also include detecting color variation among adjacent dies.By way of another example, advantages of one or more embodiments of thepresent disclosure also include improved defect detection in inspectionimages dominated by bright and/or dark noise. Advantages of one or moreembodiments of the present disclosure also include providing a procedurewith only a minimal number of operational parameters are required to betuned, allowing for a more widespread application of the presentdisclosure. Advantages of one or more embodiments of the presentdisclosure also include compatibility with other wafer inspectionprocedures. Advantages of one or more embodiments of the presentdisclosure also include increased through-put during wafer inspection bylowering computational requirements.

FIG. 1 illustrates a block diagram view of system 100 for sampleinspection, in accordance with one or more embodiments of the presentdisclosure. In one embodiment, the system 100 includes an inspectionsub-system 102. In another embodiment, the system 100 includes a samplestage 106 for securing one or more samples 104. In another embodiment,the system 100 includes a controller 110. In another embodiment, thesystem 100 includes a user interface 120.

In another embodiment, the inspection sub-system 102 is configured todetect one or more defects of the sample 104. For example, theinspection sub-system 102 may include any appropriate characterizationtool known in the art such as, but not limited to, an inspectionsub-system or review tool. For example, the inspection sub-system 102may include, but is not limited to, an optical inspection sub-system.For instance, the optical inspection sub-system may include a broadbandinspection sub-system including, but not limited to, a laser sustainedplasma (LSP) based inspection sub-system. Additionally, the opticalinspection sub-system may include a narrowband inspection sub-system,such as, but not limited to, a laser scanning inspection sub-system.Further, the optical inspection sub-system may include, but is notlimited to, a brightfield imaging tool, or a darkfield imaging tool. Itis noted herein that the inspection sub-system 102 may include anyoptical system configured to collect and analyze illumination reflected,scattered, diffracted, and/or radiated from a surface of a sample 104.By way of another example, the inspection sub-system 102 may include,but is not limited to, an electron beam inspection or review tool (e.g.,a scanning electron microscopy (SEM) system).

Examples of inspection sub-systems are described in U.S. Pat. No.7,092,082, issued on Aug. 8, 2006; U.S. Pat. No. 6,621,570 issued onSep. 16, 2003; and U.S. Pat. No. 5,805,278 issued on Sep. 9, 1998, whichare each herein incorporated by reference in the entirety. Example ofinspection sub-systems are also described in U.S. Pat. No. 8,664,594,issued on Apr. 4, 2014; U.S. Pat. No. 8,692,204, issued on Apr. 8, 2014;U.S. Pat. No. 8,698,093, issued on Apr. 15, 2014; U.S. Pat. No.8,716,662, issued on May 6, 2014; U.S. patent application Ser. No.14/699,781, filed on Apr. 29, 2015; U.S. patent application Ser. No.14/667,235, filed on Mar. 24, 2015; and U.S. patent application Ser. No.14/459,155, filed on Aug. 13, 2014, which are each herein incorporatedby reference in the entirety.

For purposes of the present disclosure, a defect may be classified as avoid, short, particle, residue, scum, or any other defect known in theart.

In another embodiment, although not shown, the inspection sub-system 102may include an illumination source, a detector and various opticalcomponents for performing inspection (e.g., lenses, beam splitters andthe like). For example, the inspection sub-system 102 may include anyillumination source known in the art. For instance, the illuminationsource may include, but is not limited to, a broadband light source or anarrowband light source. In addition, the illumination source may beconfigured to direct light to surface of the sample 104 (via variousoptical components) disposed on the sample stage 106. Further, thevarious optical components of the inspection sub-system 102 may beconfigured to direct light reflected and/or scattered from the surfaceof the sample 104 to the detector of the inspection sub-system 102. Byway of another example, the detector of the inspection sub-system 102may include any appropriate detector known in the art. For instance, thedetector may include, but is not limited to, a photo-multiplier tubes(PMTs), charge coupled devices (CCDs), time delay integration (TDI)camera, and the like. In addition, the output of the detector may becommunicatively coupled to a controller 110, described in detail furtherherein.

In another embodiment, the inspection sub-system 102 includes one ormore inspection channels. In another embodiment, at least a portion ofthe one or more inspection channels may direct illumination to onedetector. In another embodiment, the one or more inspection channels mayeach direct illumination to a detector.

In one embodiment, the sample 104 includes one or more wafers. Forexample, the sample 104 may include, but is not limited to, one or moresemiconductor wafers. As used through the present disclosure, the term“wafer” refers to a substrate formed of a semiconductor and/ornon-semi-conductor material. For instance, a semiconductor orsemiconductor material may include, but are not limited to,monocrystalline silicon, gallium arsenide, and indium phosphide.

In another embodiment, the sample stage 106 may include any appropriatemechanical and/or robotic assembly known in the art. In anotherembodiment, the controller 110 may actuate the sample stage 106. Forexample, the sample stage 106 may be configured by the controller 110 toactuate the sample 104 to a selected position or orientation. Forinstance, the sample stage 106 may include or may be mechanicallycoupled to one or more actuator, such as a motor or servo, configured totranslate or rotate the sample 104 for positioning, focusing, and/orscanning in accordance with a selected inspection or metrologyalgorithm, several of which are known to the art.

In one embodiment, the controller 110 includes one or more processors112 and a memory medium 114. In another embodiment, one or more sets ofprogram instructions 116 or stored in memory medium 114. In anotherembodiment, the one or more processors 112 are configured to execute thesets of program instructions 116 to carry out one or more of the varioussteps described throughout the present disclosure.

In another embodiment, the controller 110 is configured to receiveand/or acquire data or information from other systems or sub-systems(e.g., one or more sets of information from the inspection sub-system102 or from any of the components of the inspection sub-system 102, orone or more user inputs received via the user interface 120) by atransmission medium that may include wireline and/or wireless portions.For example, the inspection sub-system 102 or any of the components ofthe inspection sub-system 102 may transmit one or more sets ofinformation regarding the operation of the inspection sub-system 102 orany of the components of the inspection sub-system 102 to the controller110. By way of another example, the inspection sub-system 102 maytransmit one or more images of one or more inspected regions of the oneor more samples 104 to the controller 110. For instance, the one or moreimages transmitted to the controller 110 may include, but are notlimited to, one or more images of one or more inspection regions of thewafers 104.

In another embodiment, the controller 110 of the system 100 isconfigured to transmit data or information (e.g., the output of one ormore procedures disclosed herein) to one or more systems or sub-systems(e.g., one or more commands to the inspection sub-system 102 or to anyof the components of the inspection sub-system 102, the sample stage106, or one or more outputs displayed on the user interface 120) by atransmission medium that may include wireline and/or wireless portions.In this regard, the transmission medium may serve as a data link betweenthe controller 110 and other subsystems of the system 100. In anotherembodiment, the controller 110 is configured to send data to externalsystems via a transmission medium (e.g., network connection).

In another embodiment, the system 100 includes one or more encoders inthe inspection sub-system 102, where the one or more encoders aggregatethe one or more sets of information (e.g., the one or more images) priorto transmission of the one or more images to the controller 110. Inanother embodiment, the system 100 includes one or more decoders in thecontroller 110 to de-aggregate one or more sets of information (e.g.,the one or more images) transmitted by the inspection sub-system 102. Itis noted herein, however, that the controller 110 and the inspectionsub-system 102 may additionally or alternatively include encoders ordecoders, respectively.

In one example, a detector of the inspection sub-system 102 may becoupled to the controller 110 in any suitable manner (e.g., by one ormore transmission media indicated by the dotted line shown in FIG. 1)such that the controller 110 may receive the output generated by thedetector. By way of another example, if the inspection sub-system 102includes more than one detector, the controller 110 may be coupled tothe multiple detectors as described above. It is noted herein thecontroller 110 may be configured to detect one or more defects of thesample 104 using detection data collected and transmitted by theinspection sub-system 102, utilizing any method and/or algorithm knownin the art to detect defects on the wafer. For example, the inspectionsub-system 102 may be configured to accept instructions from anothersubsystem of the system 100 including, but not limited to, controller110. Upon receiving the instructions from the controller 110, theinspection sub-system 102 may perform an inspection procedure at one ormore locations (e.g. one or more regions to be inspected) of the sample104 identified in the provided instructions (i.e., the inspectionrecipe), transmitting the results of the inspection procedure to thecontroller 110.

In one embodiment, the set of program instructions 116 are programmed tocause the one or more processors 112 to receive one or more images ofthree or more die of the wafer 104 from the inspection sub-system 102.In another embodiment, the set of program instructions 116 areprogrammed to cause the one or more processors 112 to determine a medianintensity value of a set of pixel intensity values acquired from a samelocation on each of the three or more die. In another embodiment, theset of program instructions 116 are programmed to cause the one or moreprocessors 112 to determine a difference intensity value for the set ofpixel intensity values by comparing the median intensity value of theset of pixel intensity values to each pixel intensity value. In anotherembodiment, the set of program instructions 116 are programmed to causethe one or more processors 112 to group the pixel intensity values intoan intensity bin based on the median intensity value of the set of pixelintensity values. In another embodiment, the set of program instructions116 are programmed to cause the one or more processors 112 to generatean initial noise boundary based on a selected difference intensity valuein the intensity bin. In another embodiment, the set of programinstructions 116 are programmed to cause the one or more processors 112to generate a final noise boundary by adjusting the initial noiseboundary. In another embodiment, the set of program instructions 116 areprogrammed to cause the one or more processors 112 to generate adetection boundary by applying a threshold to the final noise boundary.In another embodiment, the set of program instructions 116 areprogrammed to cause the one or more processors 112 to classify one ormore pixel intensity values outside the detection boundary as a defect.

In another embodiment, the set of program instructions 116 areprogrammed to generate one or more bright noise statistical values foreach pixel on the one or more dies. In another embodiment, the set ofprogram instructions 116 are programmed to generate one or more darknoise statistical values for each pixel on the one or more dies.

In another embodiment, the set of program instructions are programmed togenerate one or more scatter plots. For example, the one or more scatterplots configured to compare one or more pixel intensity values. By wayof another example, the one or more scatter plots are configured toinclude one or more noise boundaries based on the one or more pixelintensity values.

In another embodiment, where the inspection sub-system 102 includesmultiple inspection channels, the set of program instructions 116 areprogrammed to generate one or more reconstructed images by combining theone or more images acquired by the one or more channels of theinspection sub-system. In this embodiment, the set of programinstructions 116 are programmed to classify a pixel as a defect onlywhen the pixel is detected in a least a portion of the multipleinspection channels (i.e. is present in the reconstructed images). Inthis embodiment, the generating of one or more reconstructed imagescombines multiple inspection channel information of the difference ofdefect signal and noise boundary.

In one embodiment, the one or more processors 112 of controller 110include any one or more processing elements known in the art. In thissense, the one or more processors 112 may include any microprocessordevice configured to execute algorithms and/or instructions. Forexample, the one or more processors 112 may consist of a desktopcomputer, mainframe computer system, workstation, image computer,parallel processor, vehicle on-board computer, handheld computer (e.g.tablet, smartphone, or phablet), or other computer system (e.g.,networked computer) configured to execute a program configured tooperate the system 100, as described throughout the present disclosure.It should be recognized that the steps described throughout the presentdisclosure may be carried out by a single computer system or,alternatively, multiple computer systems. In general, the term“processor” may be broadly defined to encompass any device having one ormore processing elements, which execute the program instructions 116from a non-transitory memory medium (e.g., memory 114). Moreover,different subsystems of the system 100 (e.g., inspection sub-system 102or user interface 120) may include processor or logic elements suitablefor carrying out at least a portion of the steps described throughoutthe present disclosure. Therefore, the above description should not beinterpreted as a limitation on the present disclosure but merely anillustration.

In one embodiment, the memory medium 114 of controller 110 includes anystorage medium known in the art suitable for storing the programinstructions 116 executable by the associated one or more processors112. For example, the memory medium 114 may include a non-transitorymemory medium. For instance, the memory medium 114 may include, but isnot limited to, a read-only memory, a random access memory, a magneticor optical memory device (e.g., disk), a magnetic tape, a solid statedrive and the like. In another embodiment, it is noted herein that thememory 114 is configured to provide display information to a displaydevice 122 and/or the output of the various steps described herein. Itis further noted that memory 114 may be housed in a common controllerhousing with the one or more processors 112. In an alternativeembodiment, the memory 114 may be located remotely with respect to thephysical location of the processors 112 and controller 110. Forinstance, the one or more processors 112 of controller 110 may access aremote memory (e.g., server), accessible through a network (e.g.,internet, intranet and the like). In another embodiment, the memorymedium 114 stores the program instructions 116 for causing the one ormore processors 112 to carry out the various steps described through thepresent disclosure.

In another embodiment, the user interface 120 is communicatively coupledto the one or more processors 112 of controller 110. In anotherembodiment, the user interface 120 includes a display device 122. Inanother embodiment, the user interface 120 includes a user input 124.

In one embodiment, the display device 122 includes any display deviceknown in the art. For example, the display device may include, but isnot limited to, a liquid crystal display (LCD). By way of anotherexample, the display device may include, but is not limited to, anorganic light-emitting diode (OLED) based display. By way of anotherexample, the display device may include, but is not limited to a CRTdisplay. Those skilled in the art should recognize that a variety ofdisplay devices may be suitable for implementation in the presentdisclosure and the particular choice of display device may depend on avariety of factors, including, but not limited to, form factor, cost,and the like. In a general sense, any display device capable ofintegration with the user input device (e.g., touchscreen, bezel mountedinterface, keyboard, mouse, trackpad, and the like) is suitable forimplementation in the present disclosure.

In one embodiment, the user input device 124 includes any user inputdevice known in the art. For example, user input device 124 may include,but is not limited to, a keyboard, a keypad, a touchscreen, a lever, aknob, a scroll wheel, a track ball, a switch, a dial, a sliding bar, ascroll bar, a slide, a handle, a touch pad, a paddle, a steering wheel,a joystick, a bezel input device or the like. In the case of atouchscreen interface, those skilled in the art should recognize that alarge number of touchscreen interfaces may be suitable forimplementation in the present disclosure. For instance, the displaydevice 122 may be integrated with a touchscreen interface, such as, butnot limited to, a capacitive touchscreen, a resistive touchscreen, asurface acoustic based touchscreen, an infrared based touchscreen, orthe like. In a general sense, any touchscreen interface capable ofintegration with the display portion of a display device is suitable forimplementation in the present disclosure. In another embodiment, theuser input device 124 may include, but is not limited to, a bezelmounted interface.

The embodiments of the system 100 illustrated in FIG. 1 may be furtherconfigured as described herein. In addition, the system 100 may beconfigured to perform any other steps(s) of any of the system and methodembodiment(s) described herein.

FIG. 2 illustrates a process flow diagram depicting a method 200 forinspecting a wafer with a noise boundary threshold procedure (NBTprocedure 200). The method may also include any other step(s) that canbe performed by the output acquisition subsystem and/or computersubsystem(s) or system(s) described herein. The steps may be performedby one or more computer systems, which may be configured according toany of the embodiments described herein. It is noted herein that thesteps of method 200 may be implemented all or in part by the system 100.It is recognized, however, that the method 200 is not limited to thesystem 100 in that additional or alternative system-level embodimentsmay carry out all or part of the steps of method 200.

In one embodiment, the NBT procedure 200 includes separate noisestatistics for each die of one or more dies. For example, the NBTprocedure 200 does not assume noise is common to the one or more dies.In another embodiment, the NBT procedure 200 includes separate brightnoise and dark noise statistics for each die of the one or more dies.For example, the NBT procedure 200 does not assume noise is common toboth the bright defect and dark defect. It is noted herein the noisestatistics and the bright noise and dark noise statistics may becalculated together or independently of one another. In anotherembodiment, the NBT procedure 200 does not implement a pre-defined (i.e.Gaussian) noise model.

In a step 202, one or more images of three or more dies of a wafer arereceived from an inspection sub-system 102. In another embodiment, thethree or more dies include one or more pixel intensity values.

In another embodiment, the one or more pixel intensity values are at asame location (e.g., a set of x, y coordinates based on matching axesbetween the three or more dies). It is noted herein that one or morepixel intensity values at the same location are considered a set ofpixel intensity values for purposes of the present disclosure. It isfurther noted herein there may be any number of sets of one or morepixel intensity values located at a same location on the three or moredies.

In another embodiment, the NBT procedure 200 is configured to receivethe received images include a low number of defective pixels. It isnoted herein, however, that the NBT 200 procedure may be applied toimages with any number of defective pixels. In another embodiment, theNBT procedure 200 is configured to inspect noise that changes smoothlywith pixel gray level (GL) changes. It is noted herein, however, thatthe NBT procedure 200 may inspect noise that changes unevenly with pixelGL changes. In another embodiment, the NBT procedure 200 may not detectone or more defects where the defective pixel difference intensity valueis smaller than the difference intensity value of non-defective pixelswith a similar GL. For example, the NBT procedure 200 may not detectdefects buried under a noise floor.

In step 204, a median intensity value of a set of pixel intensity valuesacquired from a same location on each of the three or more die isdetermined. In one embodiment, three or more dies are preferable toperform statistical procedures, including the procedure of finding themedian intensity value of each set of pixel intensity values. It isnoted herein, however, there may be any number of dies, including twodies. In the case of two dies, either the higher or lower value of theintensity value is selected as the “median” intensity value. Therefore,the above description should not be interpreted as a limitation on thepresent disclosure but merely an illustration.

In step 206, a difference intensity value for the set of pixel intensityvalues is determined by comparing the median intensity value of the setto each pixel intensity value. In one embodiment, the differenceintensity value is the median intensity value subtracted from each pixelintensity value. In another embodiment, the difference intensity valuemay be positive (i.e. the median intensity value is lower than theparticular pixel intensity value). In another embodiment, the differenceintensity value may be negative (i.e. the median intensity value ishigher than the particular pixel intensity value).

FIG. 3A illustrates graphical data 300 of a scatter plot, in accordancewith one or more embodiments of the present disclosure. In oneembodiment, the graphical data 300 plots the difference intensity valuefor each pixel intensity value of the set of pixel intensity valuesagainst the median intensity value of the set of pixel intensity values,as represented by T-median markers 302. In another embodiment, one ormore potential defects are represented by DOI markers 304.

In step 208, the pixel intensity values are grouped into an intensitybin based on the median intensity value of the set of pixel intensityvalues. In one embodiment, the inspection sub-system 102 has a selectintensity range. For example, the select intensity range may be [04095], or 4096 total points. It is noted herein, however, that theselect intensity range may be any number of total points. In anotherembodiment, the intensity range is separated into one or more intensitybins. For example, the NBT procedure 200 may include a configurablenumber of intensity bins based on the application of the inspectionsystem. For instance, the NBT procedure 200 may have 128 intensity bins.In another embodiment, the one or more intensity bins include aconfigurable number of pixel intensity values. For example, the NBTprocedure 200 may include 32 pixel intensity values in each intensitybin. By way of another example, the NBT procedure 200 may include asmaller intensity bin size (e.g. 4 pixel intensity values in each bin)for better noise boundary determination where the pixel dynamic range issmall (e.g., around 150 GL count). By way of another example, the NBTprocedure 200 may have a larger intensity bin size to avoid the impactfrom local noise variation where the pixel dynamic range is larger. Inanother embodiment, the one or more intensity bins are defined byselectable median intensity value ranges. For example, the medianintensity value range may be 0-100, 100-200, 200-300, and the like.

It is noted herein the NBT procedure 200 may include any number ofintensity bins, any bin pixel containment size, or any range of medianintensity value ranges. Therefore, the above description should not beinterpreted as a limitation on the present disclosure but merely anillustration.

In step 210, the NBT procedure 200 generates an initial noise boundary312 based on a selected difference intensity value in the one or moreintensity bins. For example, the selected difference intensity value maybe the largest positive difference intensity value. By way of anotherexample, the selected difference intensity value may be the smallestnegative difference intensity value in the one or more intensity bins.FIG. 3B illustrates graphical data 310 of a scatter plot including theinitial noise boundary 312, in accordance with one or more embodimentsof the present disclosure.

In another embodiment, the selected difference intensity value may bethe Nth-largest positive (or Nth-smallest negative) intensity value,where N is a selectable and configurable parameter (e.g. 8 or 10). FIG.4A illustrates graphical data 400 of a scatter plot, in accordance withone or more embodiments of the present disclosure. In one embodiment,the graphical data 400 includes one or more T-median values 402, one ormore outlier T-median values 403, one or more DOI 404, one or more noiseboundaries 406, and one or more detection boundaries 408. As illustratedin FIG. 4A, selecting the largest positive difference intensity resultsin a skewed noise boundary 406 and detection boundary 408 due the one ormore outlier T-median values 403, meaning the selecting of the largestpositive difference intensity value would result in one or more missedDOI 404. FIG. 4B illustrates graphical data 410 of a scatter plot, inaccordance with one or more embodiments of the present disclosure. Inone embodiment, the graphical data 410 includes one or more T-medianvalues 412, one or more outlier T-median values 413, one or more DOI414, one or more noise boundaries 416, and one or more detectionboundaries 418. As illustrated in FIG. 4B selecting the Nth-largestpositive difference intensity value results in a noise boundary 416 anddetection boundary 418 that is able to observe the one or more DOI 414.It is noted application of the Nth parameter may be a more precisemethod of tracking defects than selecting the largest positive (orsmallest negative) difference intensity, and further may be the onlymethod when the defect is above a certain size (i.e. is a scratch orfull missing structure) where a large number of pixel intensity valuescorresponding to defective pixels exist.

It is noted herein that the NBT procedure 200 may select multipledifference intensity values. For example, the NBT procedure 200 mayselect the largest positive difference intensity value and the2^(nd)-largest positive difference intensity value. For instance, theNBT procedure 200 may elect the 2^(nd)-largest positive differenceintensity value for the selected difference intensity value of theintensity bin. By way of another example, the NBT procedure 200 mayselect any number of difference intensity values, and additionally mayelect to keep any of the difference intensity values as the selecteddifference intensity value of the intensity bin.

In another embodiment, although not shown, a bright noise boundary isgenerated independently of the initial noise boundary 312. In anotherembodiment, although not shown, a dark noise boundary is generatedindependently of the initial noise boundary 312.

In step 212, a final noise boundary is generated by adjusting theinitial noise boundary 312. In one embodiment, an adjacent minimumintensity bin value is selected. In another embodiment, a minimumintensity value for each of the one or more intensity bins isdetermined. In another embodiment, a first adjusted noise boundary isgenerated from the initial noise boundary 312. FIG. 3C illustratesgraphical data 320 of a scatter plot including the initial noiseboundary 322, in accordance with one or more embodiments of the presentdisclosure.

For example, an adjacent minimum intensity bin value of 5 may beselected, meaning a bin its 2 left and 2 right adjacent bins. By way ofanother example, the initial noise value of the bin is adjusted to theminimum of the 5 selected adjacent bins, from which a first adjustednoise boundary is generated.

In another embodiment, an adjacent maximum intensity bin value isselected. In another embodiment, a maximum intensity value for each ofthe one or more intensity bins from the first adjusted noise boundary isdetermined. In another embodiment, a second adjusted noise boundary isgenerated from the first adjusted noise boundary.

For example, the adjacent maximum intensity bin value of 5 may first beselected, meaning a bin and its 2 left and 2 right adjacent bins. By wayof another example, the first adjusted noise value of the bin isadjusted to maximum of the 5 selected adjacent bins, from which a secondadjusted noise boundary is generated.

It is noted herein the order of adjacent minimum and adjacent maximumprocedures is dependent on whether the initial noise boundary ispositive in nature or negative in nature. For example, the minimum andmaximum procedures may be performed in the order listed on the initialnoise boundary based on the largest positive difference intensity value.In this example, the adjacent minimum intensity bin value is found toremove defect contributions from the initial noise boundary, and theadjacent maximum intensity bin value is found to raise the value ofnon-defective bins improperly lowered. By way of another example, theminimum and maximum procedures may be performed in reverse order fromthat listed on the initial noise boundary based on the smallest negativedifference intensity value. In this example, the adjacent maximumintensity bin value is found to remove defect contributions from theinitial noise boundary, and the adjacent minimum intensity bin value isfound to lower the value of non-defective bins improperly raised.

In an additional step, the second adjusted noise boundary is smoothed.In one embodiment, an adjacent mean intensity bin value is selected. Inanother embodiment, a mean intensity value for each of the one or moreintensity bins is determined. For example, the mean difference intensityvalue may be determined by averaging the second adjusted noise boundaryvalue of each intensity bin. In another embodiment, a third adjustednoise boundary is generated from the second adjusted noise boundary. Itis noted herein the third adjusted noise boundary may additionally beconsidered a smoothed noise boundary, for purposes of the presentdisclosure. FIG. 3D illustrates graphical data 330 of a scatter plotincluding the third adjusted noise boundary 332, in accordance with oneor more embodiments of the present disclosure.

In an additional step, the third adjusted noise boundary 332 isinterpolated to generate a final noise boundary. For example, in thecase of a 128-bin system, the 128-bin smoothed boundary is interpolatedto include 4096 points to generate the final noise boundary. Forexample, FIG. 5A illustrates graphical data 500 of a scatter plotincluding the interpolated noise boundary 506, in accordance with one ormore embodiments of the present disclosure.

It is noted herein that one or more of the sub-steps of step 212 may beremoved from the NBT procedure 200. For example, although embodiments ofthe present disclosure are directed to generating a first adjusted noiseboundary, a second adjusted noise boundary, and a third adjusted noiseboundary, it is noted herein that fewer than three adjusted noiseboundaries may be generated (i.e. a noise boundary based only on theminimum or maximum adjacent intensity bin value). It is further notedherein that at least a fourth adjusted noise boundary may be generatedprior to a final noise boundary, discussed in detail further herein.Therefore, the above description should not be interpreted as alimitation on the present disclosure but merely an illustration.

Therefore, the above description should not be interpreted as alimitation on the present disclosure but merely an illustration.

In step 214, a detection boundary is generated by applying (i.e. addingor multiplying) a threshold to the final noise boundary. In oneembodiment, the threshold is a configurable parameter. For example, thethreshold may be configured by the inspection sub-system 102. By way ofanother example, the threshold may be configured by a user. By way ofanother example, the threshold may be configured by the controller 110.FIG. 5A illustrates graphical data 500 of a scatter plot including thedetection or threshold noise boundary 508, in accordance with one ormore embodiments of the present disclosure.

In step 216, one or more pixels outside the detection boundary areclassified as a defect. In one embodiment, the one or more pixelintensity values outside the detection boundary correspond to one ormore pixels in the one or more images. In another embodiment, outsiderefers to pixel intensity values above the largest positive detectionboundary. In another embodiment, outside refers to pixel intensityvalues below the smallest negative detection boundary.

It is noted herein the NBT procedure 200 is not limited to a generatinga single instance of the initial noise boundary 312, the adjusted noiseboundaries 322 and 332, the final noise boundary 506, or the detectionor threshold boundary 508. For example, the NBT procedure 200 maygenerate at least two of each boundary.

FIG. 5A-5C illustrate a target die and a set of reference dies on one ormore wafers, in accordance with one or more embodiments of the presentdisclosure. FIG. 5A illustrates graphical data 500 including one or morenoise boundaries, in accordance with one or more embodiments of thepresent disclosure. In one embodiment, the graphical data 500 includesone or more T-median values 502, one or more DOI 504, the one or morefinal noise boundaries 506, and the one or more detection or thresholdboundaries 508. FIG. 5B illustrates graphical data 510 including one ormore noise boundaries, in accordance with one or more embodiments of thepresent disclosure. In one embodiment, the graphical data 510 includesone or more T-median values 512, one or more final noise boundaries 514,and one or more detection or threshold boundaries 516. FIG. 5Cillustrates graphical data 500 including one or more noise boundaries,in accordance with one or more embodiments of the present disclosure. Inone embodiment, the graphical data 520 includes one or more T-medianvalues 522, one or more final noise boundaries 524, and one or moredetection or threshold boundaries 526.

As illustrated in FIGS. 5A through 5C, the noise distribution in eachdie are different. Additionally, as illustrated in FIGS. 5A through 5C,the noise distribution in each die are different for bright and darknoise. Further, as illustrated in FIGS. 5A through 5C, the detectionboundaries 508, 516, 526 each follow the corresponding final noiseboundaries 506, 514, 524, respectively.

It is noted herein the one or more scatter plots represented in thegraphical data of FIGS. 3A-5B are conceptual illustrations, asgenerating a 2D scatter plot over the entire histogram (e.g., asimplemented by a MDAT BBP procedure) would be prohibitive to waferinspection and review speed in terms of memory and computation power. Inone embodiment, the NBT procedure 200 stores any of the initial noiseboundary 312, the adjusted noise boundaries 322, 332; the final noiseboundaries 506, 514, 524; the detection or threshold boundaries 508,516, 526; bright noise boundaries; and/or dark noise boundaries. Inanother embodiment, storing the boundaries allows the NBT procedure 200to perform fewer computational operations than the FAST procedure. Forexample, the NBT procedure 200 requires the computation power to compare1 value and assign 1 value. By way of another example, the FASTprocedure requires the computation power to determine the sum ofintensities and the square of the sum of intensities. It is furthernoted herein, however, that the NBT procedure 200 may generate one ormore scatter plots and display them on the display 122 of the userinterface 120. Therefore, the above description should not beinterpreted as a limitation on the present disclosure but merely anillustration.

It is noted herein the results of applying the NBT procedure 200 to theresults of the inspection or review of sample 106 may be used by thecontroller 110 (or another controller, a user, or a remote server) toprovide feedback or feedforward information to one or more processingtools of a semiconductor device fabrication line. In this regard, one ormore results observed or measured by the system 100 may be used toadjust process conditions at previous stages (feedback) or subsequentstages (feedforward) of the semiconductor device fabrication line.

It is further noted herein the NBT procedure 200 may be implemented intoexisting wafer inspection procedures as an independent set-up. Forexample, the NBT procedure 200 may run in parallel with other waferinspection procedures such as FAST and CF2. By way of another example,the NBT procedure 200 has a new set of wafer inspection recipeparameters, but may adopt the same data flow as other wafer inspectionprocedures. By way of another example, the NBT procedure 200 maygenerate a new set of defect attributes and add the attributes to resultbuffer. By way of another example, the detection result of the NBTprocedure 200 may be merged with result from other wafer inspectionprocedures into a final result.

All of the methods described herein may include storing results of oneor more steps of the method embodiments in a storage medium. The resultsmay include any of the results described herein and may be stored in anymanner known in the art. The storage medium may include any storagemedium described herein or any other suitable storage medium known inthe art. After the results have been stored, the results can be accessedin the storage medium and used by any of the method or systemembodiments described herein, formatted for display to a user, used byanother software module, method, or system, etc. Furthermore, theresults may be stored “permanently,” “semi-permanently,” temporarily, orfor some period of time. For example, the storage medium may be randomaccess memory (RAM), and the results may not necessarily persistindefinitely in the storage medium.

Those having skill in the art will recognize that the state of the arthas progressed to the point where there is little distinction leftbetween hardware and software implementations of aspects of systems; theuse of hardware or software is generally (but not always, in that incertain contexts the choice between hardware and software can becomesignificant) a design choice representing cost vs. efficiency tradeoffs.Those having skill in the art will appreciate that there are variousvehicles by which processes and/or systems and/or other technologiesdescribed herein can be effected (e.g., hardware, software, and/orfirmware), and that the preferred vehicle will vary with the context inwhich the processes and/or systems and/or other technologies aredeployed. For example, if an implementer determines that speed andaccuracy are paramount, the implementer may opt for a mainly hardwareand/or firmware vehicle; alternatively, if flexibility is paramount, theimplementer may opt for a mainly software implementation; or, yet againalternatively, the implementer may opt for some combination of hardware,software, and/or firmware. Hence, there are several possible vehicles bywhich the processes and/or devices and/or other technologies describedherein may be effected, none of which is inherently superior to theother in that any vehicle to be utilized is a choice dependent upon thecontext in which the vehicle will be deployed and the specific concerns(e.g., speed, flexibility, or predictability) of the implementer, any ofwhich may vary. Those skilled in the art will recognize that opticalaspects of implementations will typically employ optically-orientedhardware, software, and or firmware.

Those skilled in the art will recognize that it is common within the artto describe devices and/or processes in the fashion set forth herein,and thereafter use engineering practices to integrate such describeddevices and/or processes into data processing systems. That is, at leasta portion of the devices and/or processes described herein can beintegrated into a data processing system via a reasonable amount ofexperimentation. Those having skill in the art will recognize that atypical data processing system generally includes one or more of asystem unit housing, a video display device, a memory such as volatileand non-volatile memory, processors such as microprocessors and digitalsignal processors, computational entities such as operating systems,drivers, graphical user interfaces, and applications programs, one ormore interaction devices, such as a touch pad or screen, and/or controlsystems including feedback loops and control motors (e.g., feedback forsensing position and/or velocity; control motors for moving and/oradjusting components and/or quantities). A typical data processingsystem may be implemented utilizing any suitable commercially availablecomponents, such as those typically found in datacomputing/communication and/or network computing/communication systems.

It is believed that the present disclosure and many of its attendantadvantages will be understood by the foregoing description, and it willbe apparent that various changes may be made in the form, constructionand arrangement of the components without departing from the disclosedsubject matter or without sacrificing all of its material advantages.The form described is merely explanatory, and it is the intention of thefollowing claims to encompass and include such changes.

Although particular embodiments of this invention have been illustrated,it is apparent that various modifications and embodiments of theinvention may be made by those skilled in the art without departing fromthe scope and spirit of the foregoing disclosure. Accordingly, the scopeof the invention should be limited only by the claims appended hereto.

What is claimed:
 1. A wafer inspection system, comprising: an inspectionsub-system; a stage configured to secure a wafer; and a controllercommunicatively coupled to the inspection sub-system, wherein thecontroller includes one or more processors configured to execute a setof program instructions stored in memory, wherein the programinstructions are configured to cause the one or more processors to:receive one or more images of three or more die of the wafer from theinspection sub-system; determine a median intensity value of a set ofpixel intensity values acquired from a same location on each of thethree or more die; determine a difference intensity value for the set ofpixel intensity values by comparing the median intensity value of theset of pixel intensity values to each pixel intensity value; group eachof the pixel intensity values into an intensity bin based on the medianintensity value of the set of pixel intensity values; generate aninitial noise boundary based on a selected difference intensity value inthe intensity bin; generate a final noise boundary by adjusting theinitial noise boundary; generate a detection boundary by applying athreshold to the final noise boundary; and classify one or more pixelintensity values outside the detection boundary as a defect.
 2. Thesystem in claim 1, wherein the selected difference intensity valueincludes: a largest positive difference intensity value in the intensitybin, a smallest negative difference intensity value in the intensitybin, or a selectable difference intensity value below the largestpositive difference intensity value or above the smallest negativedifference intensity value.
 3. The system in claim 1, wherein theprogram instructions are configured to cause the one or more processorsto: select an adjacent minimum intensity bin value; group one or moreintensity bins based on the adjacent minimum intensity bin value;determine a minimum intensity value for the one or more intensity binsby comparing the intensity value of each intensity bin; generate a firstadjusted noise boundary from the initial noise boundary by reducing theintensity value of the one or more intensity bins to the minimumintensity value; select an adjacent maximum intensity bin value; groupone or more intensity bins based on the adjacent maximum intensity binvalue; determine a maximum intensity value for the one or more intensitybins by comparing the intensity value of each intensity bin; andgenerate a second adjusted noise boundary from the first adjusted noiseboundary by increasing the intensity value of the one or more intensitybins to the maximum intensity value.
 4. The system in claim 1, whereinthe program instructions are configured to cause the one or moreprocessors to: select an adjacent maximum intensity bin value; group oneor more intensity bins based on the adjacent maximum intensity binvalue; determine a maximum intensity value for the one or more intensitybins by comparing the intensity value of each intensity bin; generate afirst adjusted noise boundary from the initial noise boundary byincreasing the intensity value of the one or more intensity bins to themaximum intensity value; select an adjacent minimum intensity bin value;group one or more intensity bins based on the adjacent minimum intensitybin value; determine a minimum intensity value for the one or moreintensity bins by comparing the intensity value of each intensity bin;generate a second adjusted noise boundary from the first adjusted noiseboundary by reducing the intensity value of the one or more intensitybins to the minimum intensity value.
 5. The system in claim 1, whereinthe program instructions are further configured to cause the one or moreprocessors to: select an adjacent mean intensity bin value; group one ormore intensity bins based on the adjacent mean intensity bin value;determine a mean intensity value for the one or more intensity bins bycomparing the second adjusted noise boundary value of each intensitybin; and generate a third adjusted noise boundary from the secondadjusted noise boundary by setting the intensity value of the one ormore intensity bins to the mean intensity value.
 6. The system in claim1, wherein the program instructions are further configured to cause theone or more processors to: generate the final noise boundary byinterpolating the third adjusted noise boundary.
 7. The system in claim1, wherein the program instructions are further configured to cause theone or more processors to: determine a bright noise boundary for each ofthe three or more dies.
 8. The system in claim 1, wherein the programinstructions are further configured to cause the one or more processorsto: determine a dark noise boundary for each of the three or more dies.9. The system in claim 1, wherein the inspection sub-system comprisesone or more imaging channels, wherein the one or more imaging channelsacquire the one or more images of the three or more die of the wafer.10. The system in claim 1, wherein the program instructions are furtherconfigured to cause the one or more processors to: receive the one ormore images of the one or more die of the wafer acquired by the one ormore channels of the inspection sub-system; generate one or morereconstructed images by combining the one or more images acquired by theone or more channels of the inspection sub-system; and classify one ormore pixels outside the detection boundary in the one or morereconstructed images as a defect.
 11. A method, comprising: receivingone or more images of three or more die of a wafer from an inspectionsub-system; determining a median intensity value of a set of pixelintensity values acquired from a same location on each of the three ormore die; determining a difference intensity value for the set of pixelintensity values by comparing the median intensity value of the set ofpixel intensity values to each pixel intensity value; grouping the pixelintensity values into an intensity bin based on the median intensityvalue of the set of pixel intensity values; generating an initial noiseboundary based on a selected difference intensity value in the intensitybin; generating a final noise boundary by adjusting the initial noiseboundary; generating a detection boundary by applying a threshold to thefinal noise boundary; and classifying one or more pixel intensity valuesoutside the detection boundary as a defect.
 12. The method in claim 11,wherein the selected difference intensity value includes: a largestpositive difference intensity value in the intensity bin, a smallestnegative difference intensity value in the intensity bin, or aselectable difference intensity value below the largest positivedifference intensity value or above the smallest negative differenceintensity value.
 13. The method in claim 11, further comprising:selecting an adjacent minimum intensity bin value; grouping one or moreintensity bins based on the adjacent minimum intensity bin value;determining a minimum intensity value for the one or more intensity binsby comparing the intensity value of each intensity bin; generating afirst adjusted noise boundary from the initial noise boundary byreducing the intensity value of the one or more intensity bins to theminimum intensity value; selecting an adjacent maximum intensity binvalue; grouping one or more intensity bins based on the adjacent maximumintensity bin value; determining a maximum intensity value for the oneor more intensity bins by comparing the intensity value of eachintensity bin; and generating a second adjusted noise boundary from thefirst adjusted noise boundary by increasing the intensity value of theone or more intensity bins to the maximum intensity value.
 14. Themethod in claim 11, further comprising: selecting an adjacent maximumintensity bin value; grouping one or more intensity bins based on theadjacent maximum intensity bin value; determining a maximum intensityvalue for the one or more intensity bins by comparing the intensityvalue of each intensity bin; generating a first adjusted noise boundaryfrom the initial noise boundary by increasing the intensity value of theone or more intensity bins to the maximum intensity value; selecting anadjacent minimum intensity bin value; grouping one or more intensitybins based on the adjacent minimum intensity bin value; determining aminimum intensity value for the one or more intensity bins by comparingthe intensity value of each intensity bin; generating a second adjustednoise boundary from the first adjusted noise boundary by reducing theintensity value of the one or more intensity bins to the minimumintensity value.
 15. The method in claim 11, further comprising:selecting an adjacent mean intensity bin value; grouping one or moreintensity bins based on the adjacent mean intensity bin value;determining a mean intensity value for the one or more intensity bins bycomparing the second adjusted noise boundary value of each intensitybin; and generating a third adjusted noise boundary from the secondadjusted noise boundary by setting the intensity value of the one ormore intensity bins to the mean intensity value.
 16. The method in claim11, further comprising: generating the final noise boundary byinterpolating the third adjusted noise boundary.
 17. The method in claim11, further comprising: determining a bright noise boundary for each ofthe three or more dies.
 18. The method in claim 11, further comprising:determining a dark noise boundary for each of the three or more dies.19. The method in claim 11, wherein the inspection sub-system comprisesone or more imaging channels, wherein the one or more imaging channelsacquire the one or more images of the three or more die of the wafer.20. The method in claim 11, further comprising: receiving the one ormore images of the one or more die of the wafer acquired by the one ormore channels of the inspection sub-system; generating one or morereconstructed images by combining the one or more images acquired by theone or more channels of the inspection sub-system; and classifying oneor more pixels outside the detection boundary in the one or morereconstructed images as a defect.